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Optimization of Artificial Neural Networks in Remote Sensing Data Analysis. Tiegeng Ren Dept. of Natural Resource Science in URI (401) 874-9035 [email protected] 10/07/2002. Outline. Introduction of Satellite Remote Sensing Remote Sensing Image Classification Methodology - PowerPoint PPT Presentation
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page 1 of 50
Optimization of Artificial Neural Networks in Remote Sensing Data
Analysis
Tiegeng RenDept. of Natural Resource Science in URI
(401) 874-9035
10/07/2002
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Outline
• Introduction of Satellite Remote Sensing• Remote Sensing Image Classification
Methodology• Data and Experiments• Discussion and Conclusion
page 3 of 50
Earth Surface and Land Cover Map
How to achieve an accurate land cover map in natural resources research?
page 4 of 50
Remote SensingRemote Sensing is defined as: The science of acquiring, processing and interpreting images that record the interaction between electromagnetic energy and matter.
page 5 of 50
Different Type of Land Use and Land cover and Spectral Characteristics
Blue Band
Red Band Near-infrared Band
Green Band
page 6 of 50
From Digital Image to Land-use Map
Visualization Classification
• Multi-spectral Digital Image
• Pseudo Color Image
• Land-use and Land-cover Map
page 7 of 50
Classification System
In this study, we used the USGS classification with 10 land cover categories:
• 1. Turf/Grass
• 2. Barren land
• 3. Conifer forest
• 4. Deciduous forest
• 5. Mixed forest
• 6. Brush land
• 7. Urban land
• 8. Water
• 9. Non-forest wetland
• 10. Forest wetland
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Remote Sensing Image Classification
• Statistical Methods
– Supervised– Unsupervised
• Artificial Neural Network Approaches• Rule-based• ...
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Supervised Remote Sensing Image Classification
Vegetation
Water
Soil
Band 1 (0 ~ 255)
Band 2 (0 ~ 255)
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Classification Process
MappingRelationship
Methods:
Statistical classifier
ANN-based classifier
Observation space
SolutionspaceLandsat TM
Band1Band2Band3Band4Band5Band6Band7
0~255 0~255 0~255 0~255 0~255 0~255 0~255
Category 1Category ..Category …Category …Category …Category …Category N
WaterwetlandForestAgri.UrbanResidential
4045611938011225
Category: Forest(Pattern)
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Statistical Methods
- Need Gaussian (Normal) distribution on the input data which is required by Bayesian classifier.
- Restrictions about the format of input data.
Band 1 (0 ~ 255)
Band 2 (0 ~ 255)
Vegetation
Water
Soil
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Statistical Methods:
Deciduous forest
Conifer forest
Forest Wetland
Mixed forest
Brush Land
Non-forest Wetland
How to find a boundary for the following patterns?
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Artificial Neural Network Approach
• No need for normal distribution on input data• Flexibility on input data format• Improved classification accuracy• Robust and reliability
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……………
…………
Artificial Neural Network Is Defined by ...
• Processing elements• Organized topological structure• Learning rules
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Processing Element (PE)
f(x) OutputInput
PE
Artificial counterparts of neurons in a brain
Wj1
Wj2
Wj4
Wj3
Wj5
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PE’s Output
Function of Processing Elements
……………
…………
unitj
o1
o2
o3
fwj1
wj2
wj3
oj
PE’s Inputs
• Receive outputs from each PEs locate in previous layer.
• Compute the output with a Sigmoid activation function F(Sumof(Oi*Wji))
• Transfer the output to all the PEs in next layer
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Input layer Hidden layer Output layer
……………
…………
Input vector i(x1, x2, … xn)
Output vector i(o1, o2, … om)
Artificial Neural Network Architecture - Backpropagation ANN (BPANN)
Feed Information Analysis Result
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Pattern Recognition
Vegetation:
(10, 89) ------> (1,0,0)
(11,70) ------> (1,0,0)
… … … (1,0,0)
Water:
(10, 21) ------> (0,1,0)
(15, 32) ------> (0,1,0)
… … … (0,1,0)
Soil:
(50, 40) -------> (0,0,1)
(52, 40) -------> (0,0,1)
… … … (0,0,1)
Band 1 (0 ~ 255)
Band 2 (0 ~ 255)Vegetation
Water
Soil
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ANN Design
Pattern
Input layer Hidden layer Output layer
…………
How many PEs we need - Basic rules in designing an ANN.
• Input layer PEs - by dimension of input vector
• Output layer PEs - by total number of patterns (classes)
…………
Feed Forward
Back-Propagate
(0 ~ 255)(0 ~ 255)
(0 ~ 1)
(0 ~ 1)(0 ~ 1)
Band 1 (0 ~ 255)
Band 2 (0 ~ 255)Vegetation
Water
Soil
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ANN Training - From Pattern to Land Cover Category
Pattern
…………
…………
Feed Forward
Back-Propagate
Vegetation:
(10, 89) ------> (1,0,0)
(11,70) ------> (1,0,0)
… … … (1,0,0)
Water:
(10, 21) ------> (0,1,0)
(15, 32) ------> (0,1,0)
… … … (0,1,0)
Soil:
(50, 40) -------> (0,0,1)
(52, 40) -------> (0,0,1)
… … … (0,0,1)
Land Cover Category
10
89
1 (Vegetation)
0 (Water)
0 (Soil)
A vegetation pixel(10, 89) ------> (1,0,0)
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After Training
water
Vegetation
Soil
…………
…………
x
y
1 (Vegetation)
0 (Water)
0 (Soil)
A new pixel (x,y), x in band 1, y in band 2
A Well-trained ANN
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Problems with Traditional Back-Propagation ANN Approaches
• Time-consuming– Always over 10000 iteration, over 5 hours for a small case.
• Black box - uncontrollable training– Easily trapped in local minimum.
• Training result unpredictable
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Optimization Techniques to ANN Approach
– Data Representation - Transform to Binary and Gray code format.
– Improved Convergence Algorithms - Apply Conjugate Gradient and Resilient Propagation.
– Weight initialization - Linear Regression.
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Data Representation
Data representation - Use Binary format and gray code format instead of integer value. • Avoid to compute in the saturation range of the activation function• Increased the computation space (from 6 to 48)• Make the value more smoothly - Gray Code
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Saturation Range of Activation Function - Sigmoid Function
Saturation
Original Inputs:
0 ~ 255
New Format:
0 or 1
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Coding Method
4045611938011225
Integer format Binary format
0 0 1 0 1 0 0 0 0 0 1 0 1 1 0 10 0 1 1 1 1 0 11 1 0 0 0 0 0 10 1 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 1 0 0 1
Forest Pixel
6 Integers 48 Integers
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Gray Code vs. Binary Format
• Gray Code is another coding system, similar to binary
• Can better represent continuous value
Binary format Gray code formatInteger
127 0 1 1 1 1 1 1 1 0 1 0 0 1 0 0 0
128 1 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0
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Improved Convergence algorithms
To Make the convergence more robust and reliable, apply:
• Conjugate Gradient (CG) • Resilient Propagation (RPROP)
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Error Space and Weight Adaptive
Total Error
Steps
Wij
Wkl
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Weight Adaptive with Conjugate Gradient
Wij
Errorw
Global minimum
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Resilient Propagation (RPROP)
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Weight initialization
Cut convergence time, do weight initialization using linear regression to pre-process the internal weight, make ANN adapted to the target pattern before training.
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Data and Experiments
• Study Area
• Data Distribution
• Fine tuning the ANN structure
• Classification Result
page 34 of 50
Study Area - Rhode Island 1999 Landsat-7 Enhanced Thematic
Mapper Plus (ETM+) Image
Band 4,3,2In RGB
Band 5,4,3In RGB
page 35 of 50
Training and Testing Pattern
Training sample and Testing sample
Class Name Training SampleSize (pixels)
Testing Sample Size(pixels)
Agriculture 116 153Barren Land 146 125
Conifer Forest 173 146Deciduous Forest 343 217
Mixed Forest 265 155Brush Land 75 75Urban Area 287 108
Water 238 133Non-forest Wetland 248 163
Forest Wetland 52 57Total Pixels 1943 1332
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Distribution of Training Data
Patterns Plotted by band 3 x 4
Patterns Plotted by band 4 x 5
Deciduous forest
Turf / Grass
Barren land
Conifer forest
Forest Wetland
Mixed forest
Brush Land
Non-Forest Wetland
Water
Urban area
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ANN Design and Tuning
• Number of PEs in input layer = Number of spectral Bands in the remote sensing image• Number of PEs in output layer = Number of land cover categories• Number of PEs in hidden layer(s) to be determined for best performance
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ANN Design and Tuning- Before And After Optimization
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Classification Result
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Classification Result- A Close Look
Rhode Island 1999 ETM+ Rhode Island 1999 Land-use and Land-cover map
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Accuracy Assessment
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Discussion and Conclusion
• Accuracy
• Gray code Vs. Integer format
• Robust and reliability
• Discuss on number of hidden layer PEs
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Accuracy Comparison
Non-optimized ANN Accuracy - 79.58%
Optimized ANN Accuracy - 92.27%
page 44 of 50
Gray-code vs. Integer Value
Comparison Between Gray Code and Integer Format
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Resilient Propagation and Weight Initialization
Performance Comparison on Different Scenario
Algorithm Total Runs Out Runs Time/iterationPure BP ANN
(Conjugate gradients)30 6 >10000
Optimized ANN(Regression and
RPROP)
30 30 Average < 500
Improvement in robust and reliability
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Hidden Layer PE Number’s Influence On Classifier’s Performance.
• 48-150-10 ( 48 inputs, 150 hidden neurons, 10 output classes)• 48-250-10 ( 48 inputs, 250 hidden neurons, 10 output classes)• 48-350-10 ( 48 inputs, 350 hidden neurons, 10 output classes)
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Hidden Layer PE Number’s Influence On Classifier’s Performance.
350 hidden layer PEs provide the best performance
Benchmark on Number of Hidden Neurons
0%
20%
40%
60%
80%
100%
120%
Agricultu
re
Barr
en
Conife
r
Decid
uous
Mix
ed
Bru
sh
Urb
an
Wate
r
Non-f
ore
st W
etla
nd
Fore
st W
etla
nd
350 Hidden neurons
250 HiddenNeurons
150 Hidden neurons
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Conclusion
• Coding method, especially the Gray code is important to Remote Sensing image classification.
• Optimized ANN provide a robust and reliable classification solution
• Optimized ANN lead to higher classification accuracy
• Improved natural resources mapping
page 49 of 50
Acknowledgement
The research was funded by NASA (Grant No.
NAG5-8829) to Dr. Y.Q. Wang
• Dr. Y.Q. Wang (major professor)• Dr. Pete August (NRS, committee member)• Dr. Ken Yang (EE&CE)• Dr. Tom Boving (GEO)• Lab for Terrestrial Remote Sensing• Environmental Data Center
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Thank You!